1. Technical Field
The present invention relates to dependency networks, and, more particularly, to identifying and displaying differences between dependency networks.
2. Description of the Related Art
Network structure learning algorithms, such as Gaussian graphical models, enable scientists to visualize dependency structures in multivariate data. Recently, the problem of identifying differences in dependency networks among various classes of data has become increasingly important. For example, one neuroimaging study seeks to determine how regions of the brain share information before and after a person acquires a particular skill. The goal in this study is to identify the regions of the brain that are most influential after a skill has been learned so that direct current stimulation can be applied to those particular regions to accelerate a person's learning process. In another example, the differences between dependency structures of plasma proteins of patients that have cancer and of patients that do not have cancer have been studied to further understanding of cancer biology and to identify improved cancer diagnostics.
Traditional methods for differential dependency network analysis tend to produce a large number of spurious differences that significantly limits their usefulness. Typically, such methods are based on learning a dependency network for each task independently and then performing a comparison between them. However, large numbers of spurious differences hamper the analysis and prevent a determination of any reliable conclusions from the differential analysis. Further, these spurious differences are usually difficult to eliminate through follow-up tests.